code: def _fix_num_frames(
      sample: wsj0mix.SampleType,
      target_num_frames: int,
      sample_rate: int,
      random_start=False,
  ):
      """Ensure waveform has exact number of frames by slicing or padding"""
      mix = sample[1]  # [1, time]
      src = torch.cat(sample[2], 0)  # [num_sources, time]
  
      num_channels, num_frames = src.shape
      num_seconds = torch.div(
          num_frames, sample_rate, rounding_mode="floor"
      )
      target_seconds = torch.div(
          target_num_frames, sample_rate, rounding_mode="floor"
      )
      if num_frames >= target_num_frames:
          if random_start and num_frames > target_num_frames:
              start_frame = (
                  torch.randint(num_seconds - target_seconds + 1, [1])
                  * sample_rate
              )
              mix = mix[:, start_frame:]
              src = src[:, start_frame:]
          mix = mix[:, :target_num_frames]
          src = src[:, :target_num_frames]
          mask = torch.ones_like(mix)
      else:
          num_padding = target_num_frames - num_frames
          pad = torch.zeros(
              [1, num_padding], dtype=mix.dtype, device=mix.device
          )
          mix = torch.cat([mix, pad], 1)
          src = torch.cat([src, pad.expand(num_channels, -1)], 1)
          mask = torch.ones_like(mix)
          mask[..., num_frames:] = 0
      return mix, src, mask
  
file_path: audio/examples/source_separation/utils/dataset/utils.py
imports: from collections import namedtuple
  from functools import partial
  from typing import List
  import torch
  from torchaudio.datasets import LibriMix
  from .None import wsj0mix
label_desc: Use image node_id 2 for calling a global function with example usage: _fix_num_frames(sample, target_num_frames, sample_rate, random_start) and returns: mix,  src,  mask